code: def find_predicted_neq_given(
      labels: LabelLike,
      pred_probs: np.ndarray,
      *,
      multi_label: bool = False,
  ) -> np.ndarray:
      """A simple baseline approach that considers ``argmax(pred_probs) != labels`` as the examples with label issues.
  
      Parameters
      ----------
      labels : np.ndarray or list
        Labels in the same format expected by the `~cleanlab.filter.find_label_issues` function.
  
      pred_probs : np.ndarray
        Predicted-probabilities in the same format expected by the `~cleanlab.filter.find_label_issues` function.
  
      multi_label : bool, optional
        Whether each example may have multiple labels or not (see documentation for the `~cleanlab.filter.find_label_issues` function).
  
      Returns
      -------
      label_issues_mask : np.ndarray
        A boolean mask for the entire dataset where ``True`` represents a
        label issue and ``False`` represents an example that is accurately
        labeled with high confidence.
      """
  
      assert_valid_inputs(
          X=None,
          y=labels,
          pred_probs=pred_probs,
          multi_label=multi_label,
      )
      if multi_label:
          if not isinstance(labels, list):
              raise TypeError(
                  "`labels` must be list when `multi_label=True`."
              )
          else:
              return _find_predicted_neq_given_multilabel(
                  labels=labels, pred_probs=pred_probs
              )
      else:
          return np.argmax(pred_probs, axis=1) != np.asarray(labels)
  
imports: import numpy
  from sklearn.metrics import confusion_matrix
  import multiprocessing
  import sys
  import warnings
  from typing import Any, Dict, Optional, Tuple, List
  from functools import reduce
  import platform
  from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
  from cleanlab.rank import order_label_issues, get_label_quality_scores
  import cleanlab.internal.multilabel_scorer
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.util import value_counts_fill_missing_classes, round_preserving_row_totals, get_num_classes
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes, int2onehot
  from cleanlab.typing import LabelLike
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class
label_desc: Use image node_id 5 for calling a global function with example usage: find_predicted_neq_given(labels, pred_probs) and returns: unknown, _find_predicted_neq_given_multilabel